scholarly journals Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

2017 ◽  
Author(s):  
Esteban Alonso-González ◽  
J.¬Ignacio López-Moreno ◽  
Simon Gascoin ◽  
Matilde García-Valdecasas Ojeda ◽  
Alba Sanmiguel-Vallelado ◽  
...  
Keyword(s):  
Iberian Peninsula ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
Lapse Rate ◽  
Rate Coefficients ◽  
Balance Model ◽  
Winter Tourism ◽  
Snow Water

Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfalls occur in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse-rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism and risk management. The data presented here are available for free download from Zenodo (DOI: https://doi.org/10.5281/zenodo.854618).This paper fully describes the work flow, data validation, uncertainty assessment and possible applications and limitations of the database.

scholarly journals Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

2018 ◽  
Vol 10 (1) ◽  
pp. 303-315 ◽  
Author(s):  
Esteban Alonso-González ◽  
J. Ignacio López-Moreno ◽  
Simon Gascoin ◽  
Matilde García-Valdecasas Ojeda ◽  
Alba Sanmiguel-Vallelado ◽  
...  
Keyword(s):  
Iberian Peninsula ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
Lapse Rate ◽  
Rate Coefficients ◽  
Balance Model ◽  
Winter Tourism ◽  
Snow Water

Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km  ×  10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km  ×  10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.org/10.5281/zenodo.854618). This paper fully describes the work flow, data validation, uncertainty assessment, and possible applications and limitations of the database.

scholarly journals Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

2021 ◽  
Vol 11 (18) ◽  
pp. 8365
Author(s):  
Liming Gao ◽  
Lele Zhang ◽  
Yongping Shen ◽  
Yaonan Zhang ◽  
Minghao Ai ◽  
...  
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Implicit Scheme ◽  
Snow Water ◽  
Irtysh River ◽  
Simulation Results ◽  
The Irtysh River

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

scholarly journals An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models

2012 ◽  
Vol 4 (1) ◽  
pp. 13-21 ◽  
Author(s):  
S. Morin ◽  
Y. Lejeune ◽  
B. Lesaffre ◽  
J.-M. Panel ◽  
D. Poncet ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Model Performance ◽  
Short Wave ◽  
Wave Radiation ◽  
Density Profiles ◽  
The Public ◽  
Snow Water ◽  
Snowpack Properties

Abstract. A quality-controlled snow and meteorological dataset spanning the period 1 August 1993–31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France). Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist of measurements of air temperature, relative humidity, windspeed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September), when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN), in order to provide a continuous and consistent gap-free record. Data relevant to snowpack properties are provided at the daily (snow depth, snow water equivalent, runoff and albedo) and hourly (snow depth, albedo, runoff, surface temperature, soil temperature) time resolution. Internal snowpack information is provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles) and from a hourly record of temperature and height of vertically free ''settling'' disks. This dataset has been partially used in the past to assist in developing snowpack models and is presented here comprehensively for the purpose of multi-year model performance assessment. The data is placed on the PANGAEA repository (http://dx.doi.org/10.1594/PANGAEA.774249) as well as on the public ftp server ftp://ftp-cnrm.meteo.fr/pub-cencdp/.

scholarly journals How Do We Define Climate Change? Considering the Temporal Resolution of Niveo-Meteorological Data

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 38 ◽  
Author(s):  
Steven R. Fassnacht ◽  
Glenn G. Patterson ◽  
Niah B.H. Venable ◽  
Mikaela L. Cherry ◽  
Anna K.D. Pfohl ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
Temperature Trends ◽  
Northern Colorado ◽  
Snow Water ◽  
Snow Telemetry ◽  
Precipitation And Temperature ◽  
Precipitation Trends ◽  
Cumulative Precipitation

Historically, snowpack trends have been assessed using one fixed date to represent peak snow accumulation prior to the onset of melt. Subsequent trend analyses have considered the peak snow water equivalent (SWE), but the date of peak SWE can vary by several months due to inter-annual variability in snow accumulation and melt patterns. A 2018 assessment evaluated monthly SWE trends. However, since the month is a societal construct, this current work examines daily trends in SWE, cumulative precipitation, and temperature. The method was applied to 13 snow telemetry stations in Northern Colorado, USA for the period from 1981 to 2018. Temperature trends were consistent among all the stations; warming trends occurred 63% of the time from 1 October through 24 May, with the trends oscillating from warming to cooling over about a 10-day period. From 25 May to 30 September, a similar oscillation was observed, but warming trends occurred 86% of the time. SWE and precipitation trends illustrate temporal patterns that are scaled based on location. Specifically, lower elevations stations are tending to record more snowfall while higher elevation stations are recording less. The largest SWE, cumulative precipitation, and temperature trends were +30 to −70 mm/decade, +30 to −30 mm/decade, and +4 to −2.8 °C/decade, respectively. Trends were statistically significance an average of 25.8, 4.5, and 29.4% of the days for SWE, cumulative precipitation, and temperature, respectively. The trend in precipitation as snow ranged from +/−2%/decade, but was not significant at any station.

Development of a Snow Load Alert System, “YukioroSignal” for Aiding Roof Snow Removal Decisions in Snowy Areas in Japan

2020 ◽  
Vol 15 (6) ◽  
pp. 688-697
Author(s):  
Hiroyuki Hirashima ◽  
Tsutomu Iyobe ◽  
Katsuhisa Kawashima ◽  
Hiroaki Sano ◽  
◽  
...  
Keyword(s):  
Snow Depth ◽  
Load Distribution ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Risk Level ◽  
Residential Areas ◽  
Alert System ◽  
Snow Removal ◽  
Snow Load ◽  
Snow Water

This study developed a snow load alert system, known as the “YukioroSignal”; this system aims to provide a widespread area for assessing snow load distribution and the information necessary for aiding house roof snow removal decisions in snowy areas of Japan. The system was released in January 2018 in Niigata Prefecture, Japan, and later, it was expanded to Yamagata and Toyama prefectures in January 2019. The YukioroSignal contains two elements: the “Quasi-Real-Time Snow Depth Monitoring System,” which collects snow depth data, and the numerical model known as SNOWPACK, which can calculate the snow water equivalent (SWE). The snow load per unit area is estimated to be equivalent to SWE. Based on the house damage risk level, snow load distribution was indicated by colors following the ISO 22324. The system can also calculate post-snow removal snow loads. The calculated snow load was validated by using the data collected through snow pillows. The simulated snow load had a root mean square error (RMSE) of 21.3%, which was relative to the observed snow load. With regard to residential areas during the snow accumulation period, the RMSE was 13.2%. YukioroSignal received more than 56,000 pageviews in the snowheavy 2018 period and 26,000 pageviews in the less snow-heavy 2019 period.

scholarly journals Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

2019 ◽  
Vol 13 (12) ◽  
pp. 3413-3434 ◽  
Author(s):  
Rebecca Gugerli ◽  
Nadine Salzmann ◽  
Matthias Huss ◽  
Darin Desilets
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
Snow Accumulation ◽  
Scaling Factor ◽  
High Temporal Resolution ◽  
High Mountain ◽  
Alpine Glacier ◽  
Snow Drift ◽  
Snow Water

Abstract. Snow water equivalent (SWE) measurements of seasonal snowpack are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be inferred from neutron counts. We present the analyses of temporally continuous SWE measurements by a CRS on an alpine glacier in Switzerland (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which differed markedly in the amount and timing of snow accumulation. By combining SWE with snow depth measurements, we calculate the daily mean density of the snowpack. Compared to manual field observations from snow pits, the autonomous measurements overestimate SWE by +2 % ± 13 %. Snow depth and the bulk snow density deviate from the manual measurements by ±6 % and ±9 %, respectively. The CRS measured with high reliability over two winter seasons and is thus considered a promising method to observe SWE at remote alpine sites. We use the daily observations to classify winter season days into those dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, snowmelt) or snow densification. For each of these process-dominated days the prevailing meteorological conditions are distinct. The continuous SWE measurements were also used to define a scaling factor for precipitation amounts from nearby meteorological stations. With this analysis, we show that a best-possible constant scaling factor results in cumulative precipitation amounts that differ by a mean absolute error of less than 80 mm w.e. from snow accumulation at this site.

scholarly journals Recent fluctuations of meteorological and snow conditions in Japanese mountains

2011 ◽  
Vol 52 (58) ◽  
pp. 209-215 ◽  
Author(s):  
Satoru Yamaguchi ◽  
Osamu Abe ◽  
Sento Nakai ◽  
Atsushi Sato
Keyword(s):  
Air Temperature ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Earth Science ◽  
Meteorological Data ◽  
Winter Precipitation ◽  
Mountainous Areas ◽  
Snow Water ◽  
Notable Increase ◽  
Snow Conditions

AbstarctMeteorological data from mountainous areas of Japan have been collected by the National Research Institute for Earth Science and Disaster Prevention (NIED) for almost 20 years. The collected long-period data indicate that neither a notable increase in mean winter temperature nor a reduction in snow depth has occurred in these areas. The maximum snow depth, SDmax, and maximum snow water equivalent, SWEmax, show similar fluctuation trends, although with large year-to-year variations in value and a larger fluctuation range for SWEmax than for SDmax. This result suggests that monitoring of only SDmax in mountainous areas is not sufficient for understanding the quantitative fluctuation of water resources originating from snow. The SDmax fluctuation trends in mountainous areas sometimes differ from those in flatland areas because mountain SDmax depends more on winter precipitation than on mean winter air temperature, whereas the opposite is true for flatlands. In addition, the dependence ratio of SDmax on fluctuations in winter precipitation changes with altitude because the distributions of precipitation with air temperature change with altitude.

scholarly journals Evaluating continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on a Swiss glacier

2019 ◽  
Author(s):  
Rebecca Gugerli ◽  
Nadine Salzmann ◽  
Matthias Huss ◽  
Darin Desilets
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Mean Absolute Error ◽  
Cosmic Ray ◽  
Absolute Error ◽  
Snow Accumulation ◽  
Scaling Factor ◽  
High Temporal Resolution ◽  
Snow Drift ◽  
Snow Water

Abstract. Snow water equivalent (SWE) measurements are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be directly derived from neutron counts. In this study, we present the analyses of temporally continuous SWE measurements by a CRS on a Swiss glacier (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which were markedly different in terms of amount and timing of snow accumulation. By combining the SWE values with snow depth measurements, we calculate the daily mean density of the snowpack. The autonomous measurements overestimate SWE by +2 % ± 12 % compared to manual field observations (snow pits). Snow depth and mean density agree with manual in situ measurements with a standard deviation of ±6 % and ±8 %, respectively. In general, the cosmic ray sensor measured with high reliability during these two winter seasons and is, thus, considered an effective method to measure SWE at remote high alpine sites. We use the daily observations to break down the winter season into days either dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, melt) or snow densification. The prevailing meteorological conditions of these periods are clearly distinct for each of the classified processes. Moreover, we compare daily SWE amounts to precipitation sums from three nearby weather stations located at lower elevations, and to a gridded precipitation dataset. We determine the best-possible scaling factor for these precipitation estimates in order to reproduce the measured accumulation on the glacier. Using only one scaling factor for the whole time series, we find a mean absolute error of less than 8 cm w.e. for the reproduced snow accumulation. By applying temperature-specific scaling factors, this mean absolute error can be reduced to less than 6 cm w.e. for all stations.

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